Traditional methods, such as blurring or pixelization, have a significant drawback — in order to provide a reliable level of privacy, all semantic information of the face is essentially destroyed [1]}. Face swapping has been recognized as a powerful tool for face obfuscation that can act as an alternative. This has sparked privacy-oriented research, as face swaps seem like a promising alternative to traditional privacy protections. If a traditional obfuscation effect is weakened to where gaze direction, expression, and mouth movements can still be seen, privacy is compromised. An addition, emerging deep learning methods, including full-body identity recognition [2]}, [3]} and models specialized to overcome the obfuscations [4]}, are nearing the state of rendering traditional privacy methods obsolete.
